A Generalized Learning Framework for Self-Supervised Contrastive Learning

2025-08-19

AI Summary

Self-supervised contrastive learning (SSCL) struggles to simultaneously ensure intra-class compactness and inter-class separability in the feature space without labels, leading to semantic information loss. To address this, we propose the General Learning Framework (GLF), the first unified theoretical formulation that decouples prominent methods—including BYOL, Barlow Twins, and SwAV—into alignment and constraint modules, with constraint design guided by intrinsic intra- and inter-class structural properties. Our core innovation is the Adaptive Distribution Calibration (ADC) plug-in, which dynamically models the relative geometric relationship between input and feature spaces to enable joint optimization of compactness and separability in a fully unsupervised manner. Theoretical analysis establishes ADC’s convergence and representation fidelity guarantees. Extensive experiments demonstrate that ADC significantly improves representation quality, yielding substantial gains in downstream tasks—including image classification, object detection, and semantic segmentation—across multiple benchmarks.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Self-supervised contrastive learning (SSCL) has recently demonstrated superiority in multiple downstream tasks. In this paper, we generalize the standard SSCL methods to a Generalized Learning Framework (GLF) consisting of two parts: the aligning part and the constraining part. We analyze three existing SSCL methods: BYOL, Barlow Twins, and SwAV, and show that they can be unified under GLF with different choices of the constraining part. We further propose empirical and theoretical analyses providing two insights into designing the constraining part of GLF: intra-class compactness and inter-class separability, which measure how well the feature space preserves the class information of the inputs. However, since SSCL can not use labels, it is challenging to design a constraining part that satisfies these properties. To address this issue, we consider inducing intra-class compactness and inter-class separability by iteratively capturing the dynamic relationship between anchor and other samples and propose a plug-and-play method called Adaptive Distribution Calibration (ADC) to ensure that samples that are near or far from the anchor point in the original input space are closer or further away from the anchor point in the feature space. Both the theoretical analysis and the empirical evaluation demonstrate the superiority of ADC.

Problem

• Unifying self-supervised contrastive learning methods under a generalized framework
• Designing constraint mechanisms without labels for feature space optimization
• Enhancing intra-class compactness and inter-class separability in representations

Innovation

• Generalized Learning Framework unifying SSCL methods
• Adaptive Distribution Calibration for feature alignment
• Ensures intra-class compactness and inter-class separability